Real-Time Transmission Line Fault Monitoring Using SVRG-DDPG and Lightweight Edge Computing

Abstract

In power system intelligent fault detection, real-time monitoring is critical due to grid complexity. To address the challenges posed by the complexity of continuous action selection and gradient estimation errors in transmission line fault monitoring, we developed a Deep Deterministic Policy Gradient (DDPG) algorithm enhanced by the Stochastic Variance Reduction Gradient (SVRG) method, termed SVRG-DDPG. This algorithm leverages the SVRG technique to mitigate the gradient estimation errors typically encountered in the DDPG algorithm. Utilizing the SVRG-DDPG framework, we further developed a transmission line fault monitoring model that directly employs real-time voltage sensor data from actual transmission line environments, encompassing a range of state information from normal operation to fault conditions. To achieve real-time monitoring and optimize system performance, we also propose a transmission line fault monitoring system based on a lightweight edge computing architecture. Using real-time voltage sensor data, the SVRG-DDPG-based fault monitoring model achieves a residual error within 30 kV accuracy. To enable real-time fault diagnosis in resource-constrained edge environments, we propose a lightweight edge-cloud collaborative architecture that dynamically allocates computational resources based on fault severity and sensor data volume. The framework is validated using high-fidelity simulation data from a power grid (covering 8 fault types under 12 operational conditions, e.g., humidity >90%, load fluctuations ±40%), which aligns with the dynamic resource demands of edge devices in practical systems. Finally, our method achieves 92% accuracy in fault diagnosis with 42% lower latency compared to baselines, leveraging SVRG-enhanced DDPG for adaptive edge-cloud synchronization. Experimental results on real-world 5G-V2X data validate its suitability for low-latency transmission line monitoring.